Recently, the neural network algorithm based on a M-P neuron model is widely applied to the fields of product recommendation, image recognition, etc. The M-P neuron model is an additive neuron model, and an output value of a neuron equals to a result of nonlinear transformation of an accumulation of a bias value to the weighted sum of the inputs to a neuron, i.e., the neural network algorithm requires large number of accumulation operations and addition operations. Meanwhile, in a training process of the neural network algorithm, not only large number of accumulation operations but also subtraction operations are included.
When the traditional general processor is used to operate the neural network algorithm, or train one neural network, only two pieces of data can be added each time, so this method is low in efficiency. Moreover, when fixed-point data are used during the operation, the add overflow is also required to be processed.